model, summary = searcher.train(data, pr_auc=True, verbose=False)
stop = time()
print("time in minutes: {}".format((stop - start) / 60))
print(summary)
##Perfomance evaluation
predictions = model.predict(data, "test")
predictions
labels = data.get_labels("test")
labels
utils.plot_roc(labels, predictions, output_folder + "roc.png")
utils.plot_prec_recall(labels, predictions, output_folder + "prec.png")
print(utils.get_performance_report(labels, predictions))
Image(output_folder + "roc.png")
Image(output_folder + "prec.png")
activations = model.get_max_activations(data, "test")
logos = model.visualize_all_kernels(activations, data, output_folder)
Image(output_folder + "motif_kernel_13.png")
Image(output_folder + "activations_kernel_13.png")
Image(output_folder + "position_kernel_13.png")
Image(output_folder + "data/alu.png")
utils.save_as_meme([logo[0] for logo in logos],
output_folder + "motifs_seq.meme")
utils.save_as_meme([logo[1] for logo in logos],
output_folder + "motifs_struct.meme")
def main():
RBPs = [("data/pum2.train.positive.fasta",
"data/pum2.train.negative.fasta",
"data/pum2.test.positive.fasta",
"data/pum2.test.negative.fasta",
"PUM2"),
("data/qki.train.positive.fasta",
"data/qki.train.negative.fasta",
"data/qki.test.positive.fasta",
"data/qki.test.negative.fasta",
"QKI"),
("data/igf2bp123.train.positive.fasta",
"data/igf2bp123.train.negative.fasta",
"data/igf2bp123.test.positive.fasta",
"data/igf2bp123.test.negative.fasta",
"IGF2BP123"),
("data/srsf1.train.positive.fasta",
"data/srsf1.train.negative.fasta",
"data/srsf1.test.positive.fasta",
"data/srsf1.test.negative.fasta",
"SRSF1"),
("data/taf2n.train.positive.fasta",
"data/taf2n.train.negative.fasta",
"data/taf2n.test.positive.fasta",
"data/taf2n.test.negative.fasta",
"TAF2N"),
("data/nova.train.positive.fasta",
"data/nova.train.negative.fasta",
"data/nova.test.positive.fasta",
"data/nova.test.negative.fasta",
"NOVA")]
for entry in RBPs:
output_folder = entry[4] + "_pysster/"
if not os.path.isdir(output_folder):
os.makedirs(output_folder)
start = time()
# predict secondary structures
utils.predict_structures(entry[0], entry[0]+".struct.gz", annotate=True)
utils.predict_structures(entry[1], entry[1]+".struct.gz", annotate=True)
utils.predict_structures(entry[2], entry[2]+".struct.gz", annotate=True)
utils.predict_structures(entry[3], entry[3]+".struct.gz", annotate=True)
# load data
data = Data([entry[0]+".struct.gz", entry[1]+".struct.gz"], ("ACGU", "HIMS"))
data.train_val_test_split(0.8, 0.1999) # we need to have at least one test sequence, even though we don't need it
print(data.get_summary())
# training
params = {"kernel_len": 8}
model = Model(params, data)
model.train(data)
# load and predict test data
data_test = Data([entry[2]+".struct.gz", entry[3]+".struct.gz"], ("ACGU", "HIMS"))
predictions = model.predict(data_test, "all")
stop = time()
print("{}, time in seconds: {}".format(entry[4], stop-start))
# performance evaluation
labels = data_test.get_labels("all")
utils.plot_roc(labels, predictions, output_folder+"roc.pdf")
utils.plot_prec_recall(labels, predictions, output_folder+"prec.pdf")
print(utils.get_performance_report(labels, predictions))
# get motifs
activations = model.get_max_activations(data_test, "all")
logos, scores = [], []
for kernel in range(model.params["kernel_num"]):
logo, score = model.visualize_kernel(activations, data_test, kernel, output_folder)
logos.append(logo)
scores.append(score)
# sort motifs by importance score
sorted_idx = [i[0] for i in sorted(enumerate(scores), key=lambda x:x[1])]
with open(output_folder+"kernel_scores.txt", "wt") as handle:
for x in sorted_idx:
print("kernel {:>3}: {:.3f}".format(x, scores[x]))
handle.write("kernel {:>3}: {:.3f}\n".format(x, scores[x]))
# save model to drive
utils.save_model(model, "{}model.pkl".format(output_folder))
